{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Storage of Network Data and Topology\n",
    "\n",
    "As discussed in the first tutorial, OpenPNM uses Numpy ``ndarrays`` to store all data, and these arrays are all stored in ``dict``s, because all OpenPNM objects are subclassed python dictionaries. In this tutorial will take a deeper dive into the details of the OpenPNM data storage scheme. The sections to be covered are:\n",
    "\n",
    "- The Spreadsheet Analogy \n",
    "- Maintaining Data Integrity\n",
    "- Representing Topology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spreadsheet Analogy\n",
    "\n",
    "The best analogy for explaining data storage in OpenPNM is the humble spreadsheet.  According to this analogy you can imagine all data is stored in a table; two tables actually, one for pore data and one for throat data.  Each pore (or throat) corresponds to a row and each property corresponds to a column.  Consider the following network with 4 pores, 3 throats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import openpnm as op\n",
    "import numpy as np\n",
    "op.visualization.set_mpl_style()\n",
    "np.random.seed(0)\n",
    "pn = op.network.Demo(shape=[4, 1, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use [pandas](https://pandas.pydata.org/) to express the geometric properties as a 'spreadsheet' and pull out all pore properties that are as wide as a single column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pore_data_sheet = pd.DataFrame({k: pn[k] for k in pn.props(element='pore') if pn[k].ndim == 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now view this 'spreadsheet':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pore.coordination_number</th>\n",
       "      <th>pore.diameter</th>\n",
       "      <th>pore.max_size</th>\n",
       "      <th>pore.seed</th>\n",
       "      <th>pore.volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.474407</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.474407</td>\n",
       "      <td>0.055905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.557595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.557595</td>\n",
       "      <td>0.090773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.501382</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.501382</td>\n",
       "      <td>0.065994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.472442</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.472442</td>\n",
       "      <td>0.055213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pore.coordination_number  pore.diameter  pore.max_size  pore.seed  \\\n",
       "0                       1.0       0.474407            1.0   0.474407   \n",
       "1                       2.0       0.557595            1.0   0.557595   \n",
       "2                       2.0       0.501382            1.0   0.501382   \n",
       "3                       1.0       0.472442            1.0   0.472442   \n",
       "\n",
       "   pore.volume  \n",
       "0     0.055905  \n",
       "1     0.090773  \n",
       "2     0.065994  \n",
       "3     0.055213  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pore_data_sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column names are the properties such as **pore.volume**, and the rows correspond to the pore index, so pore ``0`` has a volume of 0.055905.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One could also extract an entire column using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.060457Z",
     "iopub.status.busy": "2021-06-24T11:25:06.059315Z",
     "iopub.status.idle": "2021-06-24T11:25:06.063503Z",
     "shell.execute_reply": "2021-06-24T11:25:06.064416Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.055905\n",
      "1    0.090773\n",
      "2    0.065994\n",
      "3    0.055213\n",
      "Name: pore.volume, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "column = pore_data_sheet['pore.volume']\n",
    "print(column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or access individual elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.071219Z",
     "iopub.status.busy": "2021-06-24T11:25:06.070328Z",
     "iopub.status.idle": "2021-06-24T11:25:06.075321Z",
     "shell.execute_reply": "2021-06-24T11:25:06.074525Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05590507143096387\n"
     ]
    }
   ],
   "source": [
    "print(pore_data_sheet['pore.volume'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{warning}\n",
    "The spreadsheet analogy is very apt, but it breaks down slightly when one considers data that is multiple columns wide such as **'pore.coords'**.  The is stored as an Np-by-3 array in the network dictionary, but spreadsheets usually require that all data is only a single column wide. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rules to Maintain Data Integrity\n",
    "\n",
    "Several rules have been implemented to control the integrity of the data. This is implemented by subclassing the `__setitem__` method of the `dict`. Each time data is assigned to a dictionary key, OpenPNM first checks the data type and dictionary key to ensure the rules are followed.  Each of these checks are explored below.\n",
    "\n",
    "#### All Values are Converted to Numpy Arrays\n",
    "Only Numpy arrays can be stored in an OpenPNM object, and any data that is written to an OpenPNM object (i.e. dictionary) will be converted to a Numpy array.  This is done to ensure that all mathematical operations throughout the code can be consistently done using vectorization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.176510Z",
     "iopub.status.busy": "2021-06-24T11:25:06.175396Z",
     "iopub.status.idle": "2021-06-24T11:25:06.180431Z",
     "shell.execute_reply": "2021-06-24T11:25:06.181503Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "pn['throat.list'] = [1, 2, 3]\n",
    "print(type(pn['throat.list']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This illustrates that the basic python list-type has been converted to a Numpy array when stored in the dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dictionary Keys Must Start With 'pore' or 'throat'\n",
    "All array names must begin with either *'pore.'* or *'throat.'* which serves to identify the type of information they contain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.204358Z",
     "iopub.status.busy": "2021-06-24T11:25:06.203478Z",
     "iopub.status.idle": "2021-06-24T11:25:06.208127Z",
     "shell.execute_reply": "2021-06-24T11:25:06.208790Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This will throw an exception since the dict name cannot start with foo\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    pn['foo.bar'] = 0\n",
    "except:\n",
    "    print('This will throw an exception since the dict name cannot start with foo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pore or throat prefix is enforced so that OpenPNM knows how long an array should be. Attempting to write an array of the wrong length will result in an error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This will throw an exception since there are 4 pores, hence all pore arrays should be 4 elements long\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    pn['pore.test'] = [0, 0, 0]\n",
    "except:\n",
    "    print('This will throw an exception since there are 4 pores, hence all pore arrays should be 4 elements long')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Any Scalars are Expanded to a Full-Length Vector\n",
    "For the sake of consistency only arrays of length *Np* or *Nt* are allowed in the dictionary. Assigning a scalar value to a dictionary results in the creation of a full length vector, either *Np* or *Nt* long, depending on the name of the array. This effectively applies the scalar value to all locations in the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.191724Z",
     "iopub.status.busy": "2021-06-24T11:25:06.190288Z",
     "iopub.status.idle": "2021-06-24T11:25:06.195032Z",
     "shell.execute_reply": "2021-06-24T11:25:06.196182Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "pn['pore.test'] = 0\n",
    "print(pn['pore.test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how the scalar value has been cast to an array of 4 elements long, one for each pore in the network. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nested Dictionary Names are Allowed\n",
    "It's possible to create nested properties by assigning a dictionary containing numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.218609Z",
     "iopub.status.busy": "2021-06-24T11:25:06.217483Z",
     "iopub.status.idle": "2021-06-24T11:25:06.222264Z",
     "shell.execute_reply": "2021-06-24T11:25:06.223411Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'species_A': array([0, 0, 0, 0]), 'species_B': array([1, 1, 1, 1])}\n"
     ]
    }
   ],
   "source": [
    "pn['pore.concentration'] = {'species_A': 0, 'species_B': 1}\n",
    "print(pn['pore.concentration'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above rule about expanding the scalar values to a numpy array have been applied.  \n",
    "\n",
    "Requesting the top level of dictionary key returns both concentrations, but they can be accessed directly too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.231472Z",
     "iopub.status.busy": "2021-06-24T11:25:06.230502Z",
     "iopub.status.idle": "2021-06-24T11:25:06.235503Z",
     "shell.execute_reply": "2021-06-24T11:25:06.236221Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(pn['pore.concentration.species_A'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also retrieve a dictionary by requesting only the element ('pore' or 'throat') and the propname ('concentration'):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.244270Z",
     "iopub.status.busy": "2021-06-24T11:25:06.243340Z",
     "iopub.status.idle": "2021-06-24T11:25:06.248217Z",
     "shell.execute_reply": "2021-06-24T11:25:06.249026Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'species_A': array([0, 0, 0, 0]), 'species_B': array([1, 1, 1, 1])}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pn['pore.concentration']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above returns a 'subdictionary' which can be indexed into:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pn['pore.concentration']['species_A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean Arrays are Treated as Labels, Numerical Arrays are Treated as Properties\n",
    "Any Boolean data will be treated as a *label* while all other numerical data is treated as a *property*. OpenPNM uses *labels* extensively as will be illustrated in a later tutorial. For this tutorial, we'll just introduce the idea.  We can create a new label by assigning `False` to a new key. This will expand the single `False` value into every pore, which means that NO pores have that label (yet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.257992Z",
     "iopub.status.busy": "2021-06-24T11:25:06.256769Z",
     "iopub.status.idle": "2021-06-24T11:25:06.261857Z",
     "shell.execute_reply": "2021-06-24T11:25:06.262995Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pore.label', 'pore.left', 'pore.right', 'pore.surface', 'pore.xmax', 'pore.xmin']\n"
     ]
    }
   ],
   "source": [
    "pn['pore.label'] = False\n",
    "print(pn.labels(element='pore'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that ``'pore.label'`` shows up in this list automatically since it is of Boolean type. However, if you ask the network which pores have that label, the answer is none:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=int32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pn.pores('label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we then set some locations to `True`, this will change:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n"
     ]
    }
   ],
   "source": [
    "pn['pore.label'][[0, 1, 2]] = True\n",
    "print(pn.pores('label'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenPNM objects include a method for creating a label and applying it to locations all in one step using the `set_label` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3]\n"
     ]
    }
   ],
   "source": [
    "pn.set_label('another', pores=[2, 3])\n",
    "print(pn.pores('another'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dictionary Keys with a Leading Underscore are Hidden\n",
    "Following the Python convention, if a piece of data is not really meant to be seen or used by the user, it can be pre-pended with an underscore and it will not appear in any output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.271759Z",
     "iopub.status.busy": "2021-06-24T11:25:06.270788Z",
     "iopub.status.idle": "2021-06-24T11:25:06.275494Z",
     "shell.execute_reply": "2021-06-24T11:25:06.276273Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "1     : pore.concentration.species_A\n",
      "2     : pore.concentration.species_B\n",
      "3     : pore.coordination_number\n",
      "4     : pore.coords\n",
      "5     : pore.diameter\n",
      "6     : pore.max_size\n",
      "7     : pore.seed\n",
      "8     : pore.test\n",
      "9     : pore.volume\n",
      "10    : throat.conns\n",
      "11    : throat.cross_sectional_area\n",
      "12    : throat.diameter\n",
      "13    : throat.diffusive_size_factors\n",
      "14    : throat.hydraulic_size_factors\n",
      "15    : throat.length\n",
      "16    : throat.lens_volume\n",
      "17    : throat.list\n",
      "18    : throat.max_size\n",
      "19    : throat.spacing\n",
      "20    : throat.total_volume\n",
      "21    : throat.volume\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
     ]
    }
   ],
   "source": [
    "pn['pore._hidden'] = 1\n",
    "print(pn.props())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``'pore._hidden'`` key does not show up in this list, though it is present:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pn['pore._hidden']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print('pore._hidden' in pn.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `param` prefix\n",
    "\n",
    "Expanding all scalar values to arrays that are either Np or Nt long is done as a convenience or shorthand to mean \"place this value of X in all pores\". It means that you can later ask \"what is the value of X in pore Y\" and get an answer. This implies that X might take on different values in each pore/throat. However, there are properties which we know are constant, such as the molecular weight or critical temperature of a pure species. In V3 we have introduced the `params` attribute to all object, such that `pn.params` is a dictionary containing such scalar values. For convenience we have also added the ability to read and write to the `params` dictionary via the main get/set method. This is illustrated below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "Parameters                          Value\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "dimensionality                      (3,)\n",
      "lattice_connectivity                6\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
     ]
    }
   ],
   "source": [
    "pn.params['lattice_connectivity'] = 6\n",
    "print(pn.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{tip} \n",
    "  **Use the \"param\" prefix:** It is possible use the `param` prefix when reading and writing to the object's dictionary. This intercepts the given key and dispatches the request to the `params` attribute. This feature is particularly useful when passing arguments to pore-scale models (to be discussed in a later tutorial) since you can pass either `arg='param.value'` or `arg='pore.value'` and OpenPNM will be able to fetch the requested data by doing `value = pn[arg]` and both options will work.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "Parameters                          Value\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
      "dimensionality                      (3,)\n",
      "lattice_connectivity                6\n",
      "test                                2\n",
      "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
     ]
    }
   ],
   "source": [
    "pn['param.test'] = 2\n",
    "print(pn.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Representing Topology\n",
    "\n",
    "Topology was introduced in the last tutorial, but not discussed in depth. Actually, the *next* tutorial will explain topology in full detail, but here we can reveal a little more about it as it pertains to data storage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the following simple random network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.285814Z",
     "iopub.status.busy": "2021-06-24T11:25:06.284955Z",
     "iopub.status.idle": "2021-06-24T11:25:06.303321Z",
     "shell.execute_reply": "2021-06-24T11:25:06.304283Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x1415.35 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1329,
       "width": 482
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "dn = op.network.Delaunay(points=4, shape=[1, 1, 0])\n",
    "op.visualization.plot_tutorial(dn);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic premise of how OpenPNM stores topology can be stated in 1 sentence:\n",
    "\n",
    "**The pores on either end of a throat are just another property to be stored, along with diameter, length, etc.**\n",
    "\n",
    "In other words, referring to the above diagram, throat No. 5 has pores 1 and 4 on its ends.  Using the spreadsheet analogy, this implies a new column that stores the pair of pores connected by each throat.  OpenPNM calls this property ``'throat.conns'``: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:25:06.713161Z",
     "iopub.status.busy": "2021-06-24T11:25:06.712287Z",
     "iopub.status.idle": "2021-06-24T11:25:06.715218Z",
     "shell.execute_reply": "2021-06-24T11:25:06.715912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1]\n",
      " [0 2]\n",
      " [0 3]\n",
      " [1 3]\n",
      " [2 3]]\n"
     ]
    }
   ],
   "source": [
    "print(dn['throat.conns'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspection of this *Nt-by-2* array shows that each row contains the pore indices corresponding to that throat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note} \n",
    "  **Topology Rules**: OpenPNM requires that the following be true about the network:\n",
    "  1. A throat is a connection between exactly two pores, no more and no less\n",
    "  2. Throats are non-directional, meaning that flow in either direction is equal\n",
    "  Other general, but non-essential rules are:\n",
    "  3. Pores can have an arbitrary number of throats, including zero; however, pores with zero throats lead to singular matrices and other problems so should be avoided.\n",
    "  4. Two pores are generally connected by no more than one throat. It is technically possible in OpenPNM to have multiple throats between a pair of pores, but it is not rigorously  supported so unintended results may arise. For instance, when converting to CSR format the dual throats are merged into one, so re-converting back results in loss of such throats.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjacency Matrices: Dense vs Sparse\n",
    "\n",
    "The topology storage scheme described above is actually an [adjacency matrix](http://en.wikipedia.org/wiki/Adjacency_matrix), in a [sparse storage format](http://en.wikipedia.org/wiki/Sparse_matrix) known as IJV or COO.  \n",
    "\n",
    "An adjacency matrix is a *Np*-by-*Np* matrix with non-zero values at location (*i*, *j*) indicating that pores *i* and *j* are connected.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenPNM Network objects have a method that generates the sparse adjacency matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 1)\t0\n",
      "  (0, 2)\t1\n",
      "  (0, 3)\t2\n",
      "  (1, 3)\t3\n",
      "  (2, 3)\t4\n"
     ]
    }
   ],
   "source": [
    "am = dn.create_adjacency_matrix(weights=dn.Ts, triu=True)\n",
    "print(am)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be converted to a *dense* array and visualized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 1 1]\n",
      " [1 0 0 1]\n",
      " [1 0 0 1]\n",
      " [1 1 1 0]]\n"
     ]
    }
   ],
   "source": [
    "am = dn.create_adjacency_matrix()\n",
    "print(am.todense())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In dense format the *Np-by-Np* shape is evident. The above matrix is symmetrical, so connections between pore *i* and *j* as well as between *j* and *i* are shown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important feature of the adjacency matrix is that it is highly sparse (mostly zeros) and can be stored with a variety of [sparse storage schemes](https://docs.scipy.org/doc/scipy/reference/sparse.html) offered by Scipy.  OpenPNM stores the adjacency matrix in the 'COO' format, which essentially stores the coordinates (I,J) of the nonzero elements in an two-column wide array.\n",
    "\n",
    "Thus the throat property called ``'throat.conns'`` is an *Nt*-by-2 array that gives the index of the two pores on either end of a given throat.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note} \n",
    "\n",
    "  **Additional Thoughts on Sparse Storage:**\n",
    "  * In pore networks there is (usually) no difference between traversing from pore *i* to pore *j* or from pore *j* to pore *i*, so a 1 is also found at location (*j*, *i*) and the matrix is symmetrical.\n",
    "  * Since the adjacency matrix is symmetric, it is redundant to store the entire matrix when only the upper triangular part is necessary.  The ``'throat.conns'`` array only stores the upper triangular information, and *i* is always less than *j*.\n",
    "  * Although this storage scheme is widely known as *IJV*, the ``scipy.sparse`` module calls this the Coordinate or *COO* storage scheme.\n",
    "  * Some tasks are best performed on other types of storages scheme, such as *CSR* or *LIL*.  OpenPNM converts between these internally as necessary, but users can generate a desired format using the ``create_adjacency_matrix`` method which accepts the storage type as an argument (i.e. ``'csr'``, ``'lil'``, etc).  For a discussion of sparse storage schemes and the respective merits, see this [Wikipedia article](http://en.wikipedia.org/wiki/Sparse_matrix).\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``conduit`` data\n",
    "\n",
    "A *conduit* in OpenPNM refers to a 'pore-throat-pore' connection, where the pores and throats are each called *elements*. These *elements* act as resistors (or conductors) in series. Each *conduit* therefor contains exactly 1 throat and 2 pores. Transport within the network occurs via these interconnected *conduits*, so the geometric properties of these elements are often needed for computing things like diffusive conductance. This is common enough that OpenPNM V3 includes a new helper function for obtaining this data, as illustrated below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.47440675 0.23720338 0.55759468]\n",
      " [0.55759468 0.25069084 0.50138169]\n",
      " [0.50138169 0.2362208  0.47244159]]\n"
     ]
    }
   ],
   "source": [
    "D = pn.get_conduit_data('diameter')\n",
    "print(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above array contains 3 columns, with the center column representing the throat diameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.23720338 0.25069084 0.2362208 ]\n"
     ]
    }
   ],
   "source": [
    "print(pn['throat.diameter'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the left and right columns represent the diameter of the pores on each end of the throat. The order of \"left\" vs \"right\" reflects the pore indices for the ``'throat.conns'`` array.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1]\n",
      " [1 2]\n",
      " [2 3]]\n"
     ]
    }
   ],
   "source": [
    "print(pn.conns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, the values for the pore elements can be obtained using numpy's fancy indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.47440675 0.55759468]\n",
      " [0.55759468 0.50138169]\n",
      " [0.50138169 0.47244159]]\n"
     ]
    }
   ],
   "source": [
    "R1_R2 = pn['pore.diameter'][pn.conns]\n",
    "print(R1_R2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which can be seen to match the diameter values in ``D`` above. "
   ]
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "vscode": {
   "interpreter": {
    "hash": "b58bd5559424689280ce24ff6229e536533c877108d283a6d2846312dfd182d7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
